Datagram turns scattered enterprise data into a governed, AI-ready asset — metadata catalog, data quality, integration and reporting in one calm, precise platform.
Tables multiply, descriptions live in people's heads, and nobody is sure which numbers to trust. Datagram was built to close that gap.
Knowledge about data leaves with the people who held it.
Columns without meaning create confusion across the data domain.
Errors and gaps go unnoticed until a report is already wrong.
ERP, MES, spreadsheets and sensors, each with its own truth.
The same technical explanations rewritten for every new team.
From the first connection to the scheduled report — catalog, quality, integration and governance share a single workspace.
Browse every table and column with technical metadata, business descriptions, sample values and ownership — searchable in seconds.
Datagram proposes descriptions, categories and fixes, transfers documentation between similar columns and flags what deserves attention.
Completeness, validity, uniqueness and freshness rules run continuously, with scores per dataset and alerts before reports break.
Drag-and-drop pipelines connect ERP, MES, databases, files and APIs — transform, join and schedule without writing glue code.
A fast, governed query workspace with schema-aware autocomplete, saved queries and results that can be shared or scheduled.
Mark critical data, assign owners, control access by role and let scheduled email rules deliver reports and quality digests on time.
Structure, types, constraints and lineage for every object, in one place.
Generate catalog reports to organise data domains and share a clear picture with the business.
Real example values beside every column make the nature of the data obvious at a glance.
Personal, financial, operational, identifier — categories keep large schemas organised and understandable.
Flag sensitive or business-critical fields for quick access, stricter rules and audit visibility.
Datagram scores how alike column names are across systems, revealing duplicates and hidden joins.
Documentation written once is propagated automatically to similar columns, system by system.
Ask which records are missing, invalid or inconsistent — then fix them at the source.
Quality digests and reports sent automatically, daily, weekly or on threshold breaches.
Preparers, reviewers and owners see exactly what they should — nothing more.
Databases, ERP, MES, files and APIs — read-only or two-way.
Metadata is scanned, profiled and catalogued automatically.
AI suggests descriptions, categories and critical flags for review.
Quality rules, owners and access policies keep it trustworthy.
Reports, digests and alerts reach the right people on schedule.
Every sector keeps its truth in a different set of systems. Datagram catalogues, measures and governs them the way each industry needs — with the regulatory and operational context already in mind.
Regulatory reporting (BCBS 239, IFRS 9) demands provable lineage from ledger to report. Datagram identifies critical data elements, scores KYC and customer master data for completeness and keeps an audit trail regulators can follow.
Billing, CDR, CRM and network-inventory data rarely agree. Datagram reconciles subscriber and asset records across systems, flags revenue-leakage patterns in rating data and enforces retention rules for traffic and location data.
Policy, claims and underwriting data live in decades of legacy systems. Datagram documents them for IFRS 17 and Solvency II lineage, scores actuarial inputs for quality and surfaces duplicate or inconsistent claimant records that feed fraud detection.
Product, price and stock must say the same thing in store, online and in the app. Datagram keeps SKU attributes and category hierarchies consistent, scores inventory feeds for freshness and builds a trusted customer 360 from POS, loyalty and e-commerce data.
Sell-in from ERP and sell-out from distributors and retailers arrive in different shapes. Datagram harmonises product master data to GS1 standards, aligns trade-promotion and demand-planning datasets and makes traceability data auditable from plant to shelf.
ERP, MES, maintenance and quality systems describe the same plant in different vocabularies. Datagram catalogues production, maintenance, materials, stock and purchasing data end to end, scores sensor and work-order feeds for completeness and freshness, and marks HSE and traceability records — PPE compliance, incident logs, batch genealogy — as critical data for audits.
Analysts have been measuring the cost of ungoverned data for a decade. The numbers point the same way.
Average yearly cost of poor data quality to an organisation. Gartner
Of total enterprise time spent on non-value-added tasks because of poor data quality and availability. McKinsey
Of organisations seeking to scale digital business will fail without a modern approach to data and analytics governance. Gartner
Data-driven organisations are 23 times more likely to acquire customers and 19 times more likely to be profitable. McKinsey Global Institute
Estimated yearly cost of poor data quality to the US economy. IBM
Of organisations suspect their customer and prospect data is inaccurate in some way. Experian Data Quality
Docker-based deployment on-premise, in your private cloud or hosted by us. Your data never has to leave your network.
Role-based access, audit trails and critical-data marking keep sensitive information visible only to those who need it.
Every recommendation stays a suggestion until a person accepts it, and every change to metadata, rules and access is logged with who, what and when.
Book a 30-minute walkthrough with our team and see your own schema inside Datagram.